docstr: returns prediction probabilities
  
  :param input_tensor: test input.
  :param training: to determine training mode in TF2.
  :param transform_data: boolean to indicate if data transformation is required.
docstr_tok: ["returns","prediction","probabilities",":","param","input_tensor",":","test","input",".",":","param","training",":","to","determine","training","mode","in","TF2",".",":","param","transform_data",":","boolean","to","indicate","if","data","transformation","is","required","."]
code: def get_output(
      self, input_tensor, training=False, transform_data=False
  ):
      """returns prediction probabilities
  
      :param input_tensor: test input.
      :param training: to determine training mode in TF2.
      :param transform_data: boolean to indicate if data transformation is required.
      """
      if transform_data or not tf.is_tensor(input_tensor):
          input_tensor = tf.constant(
              self.transformer.transform(input_tensor).to_numpy(),
              dtype=tf.float32,
          )
      if self.backend == "TF2":
          return self.model(input_tensor, training=training)
      else:
          return self.model(input_tensor)
  
file_path: DiCE/dice_ml/model_interfaces/keras_tensorflow_model.py
imports: import tensorflow
  from tensorflow import keras
  from dice_ml.model_interfaces.base_model import BaseModel
label_desc: Use image node_id 3 for calling the KerasTensorFlowModel obj's underlying member method code with example usage: obj.get_output(input_tensor, training, transform_data) and returns: self, self